Background: Molecular profiling using high-throughput omics technologies has tremendously increased our ability to interrogate complex microbial communities at the molecular level. In the context of data reuse, the FAIRification of these extensive datasets is frequently perceived as a secondary administrative task, addressed only after data analysis has been completed. However, this approach overlooks the potential benefits of early metadata integration as the procedures for processing and analyzing raw data are primarily dictated by the underlying research design and experimental conditions. Gathering interoperable research metadata at the earliest stages creates a standardized basis for managing, processing, and analyzing data enabling more efficient and reproducible FAIR workflows. Results: The single containment principle was used to develop modular containerized reproducible workflows that support the FAIR principles for research software by systematically capturing standardized metadata for each data processing step along with the resulting data products. Using defined mock metagenomic datasets as an example, we show that interoperable research metadata can be used to drive such computational workflows. By processing raw data accordingly, machine-actionable provenance chains are created that enhance the reproducibility and reusability of the resulting data products. Conclusions: A seamless integration of wet lab experiments with computational investigations is essential for a FAIR end-to-end research process. Meta-data-managed workflows prevent the need for unnecessary data manipulation. Workflow provenance registration explicates the complex multi-step methods employed for data processing and analysis. Combining FAIR principles with data provenance registration enhances the reusability of omics datasets by promoting transparency and reproducibility.